We quantified the potential effects of physiologic artifact on the estimation of EEG band power in a cohort of typically developing children in order to guide artifact rejection methods in quantitative EEG data analysis in developmental populations. High density EEG was recorded for 2 min while children, ages 2–6, watched a video of bubbles. Segments of data were categorized as blinks, saccades, EMG or artifact-free, and both absolute and relative power in the theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz) and gamma (35–45 Hz) bands were calculated in 9 regions for each category. Using a linear mixed model approach with artifact type, region and their interaction as predictors, we compared mean band power between clean data and each type of artifact. We found significant differences in mean relative and absolute power between artifacts and artifact-free segments in all frequency bands. The magnitude and direction of the differences varied based on power type, region, and frequency band. The most significant differences in mean band power were found in the gamma band for EMG artifact and the theta band for ocular artifacts. Artifact detection strategies need to be sensitive to the oscillations of interest for a given analysis, with the most conservative approach being the removal of all EMG and ocular artifact from EEG data. Quantitative EEG holds considerable promise as a clinical biomarker of both typical and atypical development. However, there needs to be transparency in the choice of power type, regions of interest, and frequency band, as each of these variables are differentially vulnerable to noise, and therefore, their interpretation depends on the methods used to identify and remove artifacts.

Attentional selection in the context of goal-directed behavior involves top-down modulation to enhance the contrast between relevant and irrelevant stimuli via enhancement and suppression of sensory cortical activity. Acetylcholine (ACh) is believed to be involved mechanistically in such attention processes. The objective of the current study was to examine the effects of donepezil, a cholinesterase inhibitor that increases synaptic levels of ACh, on the relationship between performance and network dynamics during a visual working memory (WM) task involving relevant and irrelevant stimuli. Electroencephalogram (EEG) activity was recorded in 14 healthy young adults while they performed a selective face/scene working memory task. Each participant received either placebo or donepezil (5mg, orally) on two different visits in a double-blinded study. To investigate the effects of donepezil on brain network dynamics we utilized a novel EEG-based Brain Network Activation (BNA) analysis method that isolates location-time-frequency interrelations among event-related potential (ERP) peaks and extracts condition-specific networks. The activation level of the network modulated by donepezil, reflected in terms of the degree of its dynamical organization, was positively correlated with WM performance. Further analyses revealed that the frontal-posterior theta-alpha sub-network comprised the critical regions whose activation level correlated with beneficial effects on cognitive performance. These results indicate that condition-specific EEG network analysis could potentially serve to predict beneficial effects of therapeutic treatment in working memory.